galaxyMindAiLabs/IoGPT-A1-Instruct

Model Card for IoGPT-Instruct

IoGPT-Instruct is a fine-tuned generative text model developed by GalaxyMindAiLabs, built upon the powerful Mistral-Small-3.2-24B-Instruct-2506 architecture.

It is designed to handle complex instructions with high reasoning capabilities while maintaining a user-friendly and engaging tone. The model supports multilingual capabilities including Polish, Chinese, Russian, English, Abkhazian, and Korean.

Model Details

• Model Name: IoGPT-Instruct
• Organization: GalaxyMindAiLabs
• Model Type: Text-Image-2-Text
• Base Model: mistralai/Mistral-Small-3.2-24B-Instruct-2506
• License: Apache 2.0

Model Description

This model was trained to improve accuracy in responses requiring precise information, leveraging the strong 24B parameter base of Mistral Small.

Quick Start

Option 1: You can use this model with the Hugging Face transformers library.

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
from accelerate import Accelerator

torch_device = Accelerator().device
model_checkpoint = "galaxyMindAiLabs/IoGPT-A1-Instruct"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint,torch_dtype=torch.bfloat16,  device_map="auto")
user_prompt = "Why sky is blue?"

messages = [
    {"role": "user", "content": user_prompt},
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, return_tensors="pt").to(0, dtype=torch.float16)
generate_ids = model.generate(**inputs, max_new_tokens=5000, do_sample=True) # We recommend always setting True to avoid hallucinations.
decoded_output = processor.batch_decode(generate_ids[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True)[0]

print(decoded_output)

Option 2: Using Unsloth (Faster Inference) Since this model was trained with Unsloth, using their library provides 2x faster inference.

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "galaxyMindAiLabs/IoGPT-A1-Instruct",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

inputs = tokenizer(
[
    "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)
print(tokenizer.batch_decode(outputs))

Training Procedure This model was trained using Unsloth and TRL (Transformer Reinforcement Learning). Key Improvements: * Reasoning: Enhanced logical consistency inherited from the 24B Mistral base. * Tone: Fine-tuned for a helpful, polite, and precise assistant persona. * Multilingualism: Improved handling of diverse languages listed above. Framework Versions * TRL: 0.24.0 * Transformers: 4.57.6 * Pytorch: 2.10.0 * Datasets: 4.3.0 * Tokenizers: 0.22.2

Citations

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{[https://github.com/huggingface/trl](https://github.com/huggingface/trl)}}
}